New rule based classification algorithm for automobile insurance fraud detection
العناوين الأخرى
خوارزمية جديدة للتصنيف المبني على القواعد في اكتشاف احتيال التأمين على المركبات
مقدم أطروحة جامعية
مشرف أطروحة جامعية
أعضاء اللجنة
al-Hamami, Ala Husayn
al-Jabir, Fadi Abd
al-Awanih, Ali
الجامعة
جامعة فيلادلفيا
الكلية
كلية تكنولوجيا المعلومات
القسم الأكاديمي
قسم علم الحاسوب
دولة الجامعة
الأردن
الدرجة العلمية
ماجستير
تاريخ الدرجة العلمية
2013
الملخص الإنجليزي
-Automobile Insurance Fraud (AIF) is a significant and costly problem for both policyholders and insurance companies.
The fraudulent activities may affect negatively on the profits of automobile insurance companies.
Data mining especially rule based classification algorithms can contribute in helping the detection of fraudulent activities.
In these algorithms the output is represented in simple interpreted "If-Then" knowledge and stored in a knowledge base.
However, the problem of rule based classification such as (PRISM) generates large number of rules.
Since maintaining and understanding these classifier rules depend on classifiers size which is hard by the typical end user.
Moreover, some correlation rules in (PRISM) that near perfection ones can't be extracted.
These disappeared rules in competitive environment are considered very significant in the prediction phase.
On the other hand, induction rule based algorithm i.e.
Repeated Incremental Pruning to Produce Error Reduction (RIPPER) have small size classifiers with often low accuracy, these rules is not feasible regarding to the (AIF) classification problem, because some knowledge are undetected.
This thesis investigates the applicability of strength threshold based covering method on the problem of detection the accident type in order to make balance in producing the number of generated rules without impacting on the classification rate.
The new algorithm named Strength Threshold Based Coverage Prism (STBCP) that makes balance, (as a result on average size classifiers) in producing the rules.
This balance is accomplished by producing a new rule based classification algorithm (STBCP) that utilized a new learning, pruning and prediction procedures based on different strength threshold values (2%, 3%, 4%, 5%, and 6%) against "autos" data set using significant and complete features (More details in Chapter Three).
Based on those threshold values (2-6%), the experimental results found that, the (STBCP) algorithm produced the highest accurate classifier than PRISM, RIPPER and J.48 decision tree algorithms.
We chose (4% as average of threshold values) and we found that, STBCP algorithm produced the highest accuracy compared with PRISM, RIPPER and J.48 decision tree algorithms.
In general, the STBCP algorithm produces neither in large nor in small numbers of rules xiii (classifiers), but it make balance between them (as a result on average size).
These allow end user and decision makers to maintain and understand the produced rules with a clear representation without impacting on the classification rate (accuracy).
التخصصات الرئيسية
تكنولوجيا المعلومات وعلم الحاسوب
الموضوعات
عدد الصفحات
73
قائمة المحتويات
Table of contents.
Abstract.
Abstract in Arabic.
Chapter One : Introduction.
Chapter Two : Literature review.
Chapter Three : The proposed model (STBCP).
Chapter Four : Conclusions and future works.
References.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
al-Ali, Ahmad Uqlah Ali. (2013). New rule based classification algorithm for automobile insurance fraud detection. (Master's theses Theses and Dissertations Master). Philadelphia University, Jordan
https://search.emarefa.net/detail/BIM-544029
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
al-Ali, Ahmad Uqlah Ali. New rule based classification algorithm for automobile insurance fraud detection. (Master's theses Theses and Dissertations Master). Philadelphia University. (2013).
https://search.emarefa.net/detail/BIM-544029
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
al-Ali, Ahmad Uqlah Ali. (2013). New rule based classification algorithm for automobile insurance fraud detection. (Master's theses Theses and Dissertations Master). Philadelphia University, Jordan
https://search.emarefa.net/detail/BIM-544029
لغة النص
الإنجليزية
نوع البيانات
رسائل جامعية
رقم السجل
BIM-544029
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر